Mobile devices have become an important part of our lives. People often carry mobile devices wherever they go and customize devices according to their personal preferences, environment, and/or habits. Modern mobile devices typically include various types of sensors and other data input components. These sensors gather data about the environment surrounding the mobile device and/or the user of the mobile device, and may provide information regarding device location, motion, and/or other activities. A user may also utilize a mobile device in connection with a variety of applications. Monitoring such interactions may provide additional information regarding a user's activities.
Information regarding a mobile device user's activities may be utilized in a variety of contexts. For example, advertisement-based content distribution systems may be utilized to help fund the production of content, the services that distribute the content, and the devices that render the content. To maximize the benefit of ad-based content distributions systems, advertisements delivered to a consumer should ideally be well-matched to the interests of the consumer at a time and/or location that the consumer is likely to purchase advertised goods and/or services. Similarly, services that distribute offers, promotions, or other services seek to target the distribution of such materials to those individuals most likely to be interested. Identifying and/or predicting user activities may thus allow for more effective targeting of advertisements and other personalized services to the user.
Various systems and methods disclosed herein may be utilized to recognize and/or predict activity associated with a mobile device and/or a user thereof. In some embodiments, the disclosed systems and methods may recognize activities of a user of a mobile device based on sensor information and/or other data provided by the mobile device. In certain embodiments, the systems and methods may be utilized in connection with predicting a future activity and/or location of the user based on current and/or historical device data and/or other personal information relating to the user. In some embodiments, statistical Markov models and/or the like are used. Further embodiments of the systems and methods disclosed herein may utilize location and/or activity recognition and/or prediction methods to deliver personalized services to a user of a mobile device at a particular time and/or location (e.g., targeted advertising services, services that provide offers, promotions, messages, warnings, and/or the like).
In certain embodiments, a computer-implemented method may include receiving device data collected from a mobile device and determining a first set of user activities based on the device data. Based on the first set of user activities and available device data, a second set of likely future user activities may be determined.
In some embodiments, an activity recognition system is disclosed. The activity recognition system may include a transceiver and a data analyzer. The transceiver may be configured to receive device data collected by a mobile device. The device data may be indicative of one or more user activities. Exemplary user activities may include, without limitation, walking, running, driving, shopping, dining, watching television, visiting a location, traveling, listening to music, browsing the Internet, sleeping, and/or the like. The data analyzer may be further configured to determine and/or predict one or more activities of a user associated with the mobile device based on device data. The system may further include a personalized service engine configured to provision personalized services to a user based on recognized and/or predicted activities.
In certain embodiments, a mobile device is disclosed. The mobile device may include a personal agent configured to collect device data. The device data may be indicative of one or more activities of a user associated with the mobile device. Further, the device data can be utilized to determine one or more parameters related to the user. Exemplary parameters may include, without limitation, the user's name, age, home address, work address, gender, items, activities, people, places, and/or things that the user is interested in, user behavior patterns, the frequency that the user visits a place, and/or other personal attributes that may characterize the user in some manner.
In further embodiments, a method for recognizing user activities is disclosed. In some embodiments, the method may include identifying one or more first candidate places corresponding to a user associated with a mobile device based on device data collected by one or more sensors of the mobile device. A current location of the user may be determined from one or more first candidate places based on the device data and one or more user activities. A personalized service may be provided to the user based on the determination.